Cancer Incidence Trends in Successive Social Generations in the US

Key Points Question Is cancer incidence in successive social generations in the US slowing or growing? Findings In this cohort study of 3.8 million patients with cancer ascertained by the Surveillance, Epidemiology, and End Results Program, members of Generation X born between 1965 and 1980 have been experiencing larger per-capita increases in the incidence of leading cancers combined than any prior generation born between 1908 and 1964. Meaning These findings suggest that based on current trajectories, cancer incidence in the US might remain high for decades.


Semi-Parametric Age-Period-Cohort (SAGE) Analysis
We analyzed each Lexis diagram using Semi-parametric age-period-cohort (SAGE) analysis.The basic idea is we denoise the observed Lexis diagrams up front using a contemporary nonparametric procedure, and then we fit the "new" age-period-cohort (APC) model to the smoothed Lexis diagrams.The New APC model contain parameters that describe how the expected rates vary as a function of age, period and birth cohort, assuming only that these effects are additive on the log scale.Informative combinations of these parameters, called "Estimable Functions" (EFs), are then calculated from the parameters of the New APC Model.
Because our contemporary nonparametric procedure provides a highly effective denoising tool, EFs obtained from the smoothed rates are more accurate (i.e., lower mean squared error).This allows us to obtain stable estimates from relatively sparse single-year Lexis diagrams, for example, Lexis diagrams for less common cancer types or numerically smaller race and ethnicity groups.
A schematic of the SAGE procedure is provided here.In brief, SAGE partitions the data into three pieces: APC Fitted Rates, Lack-of-Fit (LOF), and Pure Error.APC Fitted Rates obtain from "Key Parameters" and "Higher-Order Terms".Together, these parameters represent the expected values of the rates as a function of age, times a function of period, times a function of cohort.The "Higher-Order Terms" describe year-over-year fluctuations in expected incidence over or above the underlying linear and quadratic trends by age, period, and birth cohort.
The model fit is adequate when the LOF is small relative to these Higher-Order Terms.

Fitted Cohort Pattern (FCP)
The Fitted Cohort Pattern (FCP) is a model-based estimate of the rate per 100,000 person-years at an arbitrary reference age (here, age 60 years) as a function of birth cohort, adjusted for period effects.
The FCP can be interpreted as a composite curve that includes backward projections for older cohorts and forward projections for younger cohorts.
For example, in the 29-year time frame of our studywe analyzed cases diagnosed between 1992 -2018cohorts born 1932 -1958 were directly observed at age 60 years; this window spans most of the Silent Generation and the first half of the Baby Boomers.However, cohorts born from 1908 -1931 were older than age 60 years at the start of our study in 1992, and cohorts born from 1959 -1983 were younger than age 60 years at the end of our study in 2018.For them, the FCP projects backward to age 60 for the older cohorts and forward to age 60 for the latter.Importantly, these projections are calculated using all of the model parameters which were estimated from all of the data.
The amount of extrapolation varies by birth year: 1 year back for the 1931 cohort, 1 year forward for the 1959 cohort, 2 years back for 1930 cohort, 2 years forward for the 1960 cohort, etc.Therefore, in our analysis, we are incorporating from 1 -6 years of forward extrapolation for the Baby Boomer cohorts born from 1959 -1964, and from 7 -22 years of forward extrapolation for Generation X cohorts born from 1965 -1980.It is appropriate to interpret the FCP when the model fit is adequate.That is why this Supplement contains a detailed analysis of LOF.
Organ-Specific Site Code Used in the Classification of Cancer Sites Using SEER*Stat 8.4.0 eTable 2. Age-Standardized Incidence Rates and Cancer Cases by Sex, Race and Ethnicity, United States, 35-84 Years Old, 1992-2018 eFigure 1. Observed Rates in Females eFigure 2. APC Fitted Values in Females eFigure 3. Observed Rates in Males eFigure 4. APC Fitted Values in Males eFigure 5. Lack of Fit (LOF) in Females eFigure 6. Lack of Fit (LOF) in Males eFigure 7. Higher-Order Deviations vs Lack of Fit (LOF) in Females eFigure 8. Higher-Order Deviations vs Lack of Fit (LOF) in Males eFigure 9. Local Drifts in Females eFigure 10.Local Drifts in Males eFigure 11.Fitted Cohort Patterns (FCPs) by Cancer Site, Race, and Ethnicity: Females eFigure 12.Estimated Annual Percentage Change (EAPC) of the Fitted Cohort Pattern (FCP): Females eFigure 13.Fitted Cohort Patterns (FCPs) by Cancer Site, Race, and Ethnicity: Males eFigure 14.Estimated Annual Percentage Change (EAPC) of the Fitted Cohort Pattern (FCP): Males eFigure 15.Average Incidence at Age 60: Generation X vs Baby Boomers eFigure 16.Average Incidence at Age 60: Baby Boomers vs the Silent Generation eFigure 17.Average Incidence at Age 60: Silent vs Greatest Generations eFigure 18. Site-Adjusted Cancer Incidence Rate Ratios (IRRs) for Non-Hispanic Black, Hispanic, and Asian or Pacific Islander vs Non-Hispanic White by Sex and Social Generation eFigure 19.Percent Changes in Incidence of Leading Cancers at Age 60 Years per 100 000 Person-Years in Successive Generations